--- language: fa datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 widget: - example_title: Common Voice sample 687 src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac - example_title: Common Voice sample 1671 src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample1671.flac model-index: - name: XLSR Wav2Vec2 Persian (Farsi) by Mehrdad Farahani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice fa type: common_voice args: fa metrics: - name: Test WER type: wer value: 32.20 --- # Wav2Vec2-Large-XLSR-53-Persian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: **Requirements** ```bash # requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer !pip install hazm ``` **Prediction** ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import numpy as np import hazm import re import string import IPython.display as ipd _normalizer = hazm.Normalizer() chars_to_ignore = [ ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", ".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„' ] # In case of farsi chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) chars_to_mapping = { 'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', 'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", "ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه", 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", } def multiple_replace(text, chars_to_mapping): pattern = "|".join(map(re.escape, chars_to_mapping.keys())) return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) def remove_special_characters(text, chars_to_ignore_regex): text = re.sub(chars_to_ignore_regex, '', text).lower() + " " return text def normalizer(batch, chars_to_ignore, chars_to_mapping): chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" text = batch["sentence"].lower().strip() text = _normalizer.normalize(text) text = multiple_replace(text, chars_to_mapping) text = remove_special_characters(text, chars_to_ignore_regex) batch["sentence"] = text return batch def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device) dataset = load_dataset("common_voice", "fa", split="test[:1%]") dataset = dataset.map( normalizer, fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) max_items = np.random.randint(0, len(result), 20).tolist() for i in max_items: reference, predicted = result["sentence"][i], result["predicted"][i] print("reference:", reference) print("predicted:", predicted) print('---') ``` **Output:** ```text reference: اطلاعات مسری است predicted: اطلاعات مسری است --- reference: نه منظورم اینه که وقتی که ساکته چه کاریه خودمونه بندازیم زحمت predicted: نه منظورم اینه که وقتی که ساکت چی کاریه خودمونو بندازیم زحمت --- reference: من آب پرتقال می خورم لطفا predicted: من آپ ارتغال می خورم لطفا --- reference: وقت آن رسیده آنها را که قدم پیش میگذارند بزرگ بداریم predicted: وقت آ رسیده آنها را که قدم پیش میگذارند بزرگ بداریم --- reference: سیم باتری دارید predicted: سیم باتری دارید --- reference: این بهتره تا اینکه به بهونه درس و مشق هر روز بره خونه شون predicted: این بهتره تا اینکه به بهمونه درسومش خرروز بره خونه اشون --- reference: ژاکت تنگ است predicted: ژاکت تنگ است --- reference: آت و اشغال های خیابان predicted: آت و اشغال های خیابان --- reference: من به این روند اعتراض دارم predicted: من به این لوند تراج دارم --- reference: کرایه این مکان چند است predicted: کرایه این مکان چند است --- reference: ولی این فرصت این سهم جوانی اعطا نشده است predicted: ولی این فرصت این سحم جوانی اتان نشده است --- reference: متوجه فاجعهای محیطی میشوم predicted: متوجه فاجایهای محیطی میشوم --- reference: ترافیک شدیدیم بود و دیدن نور ماشینا و چراغا و لامپهای مراکز تجاری حس خوبی بهم میدادن predicted: ترافیک شدید ی هم بودا دیدن نور ماشینا و چراغ لامپهای مراکز تجاری حس خولی بهم میدادن --- reference: این مورد عمل ها مربوط به تخصص شما می شود predicted: این مورد عملها مربوط به تخصص شما میشود --- reference: انرژی خیلی کمی دارم predicted: انرژی خیلی کمی دارم --- reference: زیادی خوبی کردنم تهش داستانه predicted: زیادی خوبی کردنم ترش داستانه --- reference: بردهای که پادشاه شود predicted: برده ای که پاده شاه شود --- reference: یونسکو predicted: یونسکو --- reference: شما اخراج هستید predicted: شما اخراج هستید --- reference: من سفر کردن را دوست دارم predicted: من سفر کردم را دوست دارم ``` ## Evaluation The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import hazm import re import string _normalizer = hazm.Normalizer() chars_to_ignore = [ ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?", ".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„' ] # In case of farsi chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) chars_to_mapping = { 'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', 'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", "ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع", "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه", 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش", 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ", "\\u200c": " ", "\\u200d": " ", "\\u200e": " ", "\\u200f": " ", "\\ufeff": " ", } def multiple_replace(text, chars_to_mapping): pattern = "|".join(map(re.escape, chars_to_mapping.keys())) return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text)) def remove_special_characters(text, chars_to_ignore_regex): text = re.sub(chars_to_ignore_regex, '', text).lower() + " " return text def normalizer(batch, chars_to_ignore, chars_to_mapping): chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]""" text = batch["sentence"].lower().strip() text = _normalizer.normalize(text) text = multiple_replace(text, chars_to_mapping) text = remove_special_characters(text, chars_to_ignore_regex) batch["sentence"] = text return batch def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000) batch["speech"] = speech_array return batch def predict(batch): features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids)[0] return batch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian") model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device) dataset = load_dataset("common_voice", "fa", split="test") dataset = dataset.map( normalizer, fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping}, remove_columns=list(set(dataset.column_names) - set(['sentence', 'path'])) ) dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict) wer = load_metric("wer") print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) ``` **Test Result:** - WER: 32.20% ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)